Machine learning-based diagnosis in laser resonance frequency analysis for implant stability of orthopedic pedicle screws

Katsuhiro Mikami, Mitsutaka Nemoto, Takeo Nagura, Masaya Nakamura, Morio Matsumoto, Daisuke Nakashima

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Evaluation of the initial stability of implants is essential to reduce the number of implant failures of pedicle screws after orthopedic surgeries. Laser resonance frequency analysis (L-RFA) has been recently proposed as a viable diagnostic scheme in this regard. In a previous study, L-RFA was used to demonstrate the diagnosis of implant stability of monoaxial screws with a fixed head. However, polyaxial screws with movable heads are also frequently used in practice. In this paper, we clarify the characteristics of the laser-induced vibrational spectra of polyaxial screws which are required for making L-RFA diagnoses of implant stability. In addition, a novel analysis scheme of a vibrational spectrum using L-RFA based on machine learning is demonstrated and proposed. The proposed machine learning-based diagnosis method demonstrates a highly accurate prediction of implant stability (peak torque) for polyaxial pedicle screws. This achievement will contribute an important analytical method for implant stability diagnosis using L-RFA for implants with moving parts and shapes used in various clinical situations.

Original languageEnglish
Article number7553
JournalSensors
Volume21
Issue number22
DOIs
Publication statusPublished - 2021 Nov 1
Externally publishedYes

Keywords

  • Laser resonance frequency analysis
  • Orthopedics
  • Pedicle screw
  • Stability diagnosis

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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